One of the most active research topics in video understanding is temporal action detection, which focuses on both classifying the action instances present in an untrimmed video and localizing them with temporal boundaries.
The temporal action detection task, like object detection, is divided into two parts: temporal action proposal generation (TAPG) and action recognition.
Deep learning has recently been shown to significantly improve action recognition performance [1]}, [2]}, [3]}.
However, the performance of the two-stage temporal action detectors in mainstream benchmarks [4]}, [5]} still has much room for improvement, which is mostly influenced by the quality of proposals from temporal action proposal generation.
